• 수면정신생리
• /
• 제8권2호
• /
• pp.138-143
• /
• 2001

Sleepiness is associated with many different conditions and, as a neglected topic, it can be the cause of serious psychological and social disadvantages. In the aspect of learning, additional problems may arise from poor progress in school caused by the effect of sleepiness on concentration, memory, and other cognitive functions. Narcolepsy is by no means the most common cause of excessive sleepiness. Nonetheless, it is not a rarity, especially in young people. The non-specific nature of early features of narcolepsy, combined with very limited awareness that the condition can start in various ways, leads to many misinterpretations. Misinterpretation of narcolepsy symptoms is not confined to the medical profession. Teachers may well be critical of a student with narcolepsy because of their perception of narcolepsy symptoms as laziness, poor motivation, or difficult behavior and dull learning ability. Inappropriate reactions by parents, teachers, and peers, based on misinterpretation of narcolepsy symptoms or the patient's reactions to them, make a difficult situation worse. Especially in Korea, where schooling is focused on college entrance examinations, the problem is very serious and intensified by inappropriate or delayed diagnosis and treatment. Therefore, psychiatrists should be aware that narcolepsy in young adolescents is not rare and that they need to be familiar with its clinical features in both its classic and less obvious forms. Narcolepsy should be suspected if a adolescent's excessive sleepiness can not be explained in other ways. Therefore, we report on two patients who portray the tendency of dull learning ability and are mistaken as idle students. We diagnosed narcolepsy through polysomnography and multiple sleep latency testing. We treated the students with methylphenidate and pemolin. The students showed improvement in learning ability and were able to adapt better to school.

• Annals of Rehabilitation Medicine
• /
• 제41권6호
• /
• pp.924-934
• /
• 2017

Objective To investigate the anatomical correlates of the neuropsychological deficits in patients with the cerebellar stroke. Methods We screened patients who were admitted to the National Rehabilitation Center with the cerebellar stroke between October 2012 and November 2016. The patients with the cerebellar stroke who underwent neuropsychological testing for which the Seoul Neuropsychological Screening Battery (SNSB) or the SNSB-II were enrolled. The neuropsychological function capacities were compared in accordance with the stroke type (hemorrhagic vs. ischemic) and the location (right/left anterior, right/left posterior intermediate, right/left posterior lateral lobe, and vermis). Mean z-scores were computed to compare the patient performances with the population averages. Results Twenty-six patients (15 with ischemic stroke and 11 with hemorrhagic stroke) with a mean age of $54.8{\pm}16.6$ years were assessed $8.8{\pm}9.2$ months after the stroke. Differences in the neuropsychological functioning according to the stroke type were not observed. All of the numerical subtests of the stroke patients showed significantly poorer performances compared with the population averages (mean z-score <0), and some of the subtests revealed abnormal performances in attention-, visuospatial function-, memory-, and frontal/executive function-related tasks (mean z-score <-1). The patients with the presence of a lesion in the right posterior intermediate lobe of the cerebellum showed a poorer performance in the subtests evaluating the executive function including the Korean-version Stroop Test (p=0.04), the Digit Symbol Coding Test (p=0.01), and the Korean-version Trail Making Test (p=0.02) compared with the patients without that lesion. Conclusion The present study confirms that the cerebellar stroke affects the neuropsychological functioning which is associated with the anatomical site of stroke.

• Genomics & Informatics
• /
• 제19권1호
• /
• pp.11.1-11.8
• /
• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• 문화기술의 융합
• /
• 제8권1호
• /
• pp.513-522
• /
• 2022

본 연구는 Cakewalk by Bandlab(과거의 Sonar)의 대표적인 가상악기인 TTS-1의 접근성을 평가하고 개선방안을 제안하기 위한 목적으로 수행되었다. 본 연구는 소프트웨어 개발자의 관점에서 스크린리더 사용자가 TTS-1을사용하여 NCS의 [가상악기 에디팅하기] 능력단위 요소를 수행하기 위한 접근성이 평가되었고, 기본 접근성이 없는 기능들은 센스리더와 MIDI 기능을 활용하여 확장된 접근 방법을 모색하였다. 본 연구결과, 스크린리더 사용자가 TTS-1을 사용하여 정안인과 동등한 수준의 작업은 가능하나, 센스리더와 MIDI를 활용한 별도의 접근방법을 사용해야만 하는 것으로 나타났다. 이 결과를 통해, 스크린리더 사용자가 보다 효율적으로 작업할 수 있는 개선방안이 제안되었다.

• 생물정신의학
• /
• 제29권2호
• /
• pp.33-39
• /
• 2022

Objectives The early growth response 3 (EGR3) gene located in chromosome 8p21.3 is one of the susceptibility loci in many psychiatric disorders. EGR3 gene plays critical roles in signal transduction in the brain, which is involved in neuronal plasticity, neuronal development, learning, memory, and circadian rhythms. Recent studies have suggested EGR3 as a potential susceptibility gene for bipolar disorder (BPD). However, this requires further replication with an independent sample set. Methods To investigate the genetic role of EGR3 in Korean patients, we genotyped six single-nucleotide polymorphisms (SNPs) in the chromosome region of EGR3 in 1076 Korean BPD patients and 773 healthy control subjects. Results Among the six examined SNPs of EGR3 (rs17088531, rs1996147, rs3750192, rs35201266, rs7009708, rs1008949), SNP rs35201266, rs7009708, rs1008949 showed a significant association with BPD (p = 0.0041 for rs35201266 and BPD2, p = 0.0074 for rs1008949 and BPD, p = 0.0052 for rs1008949 and BPD1), which withstand multiple testing correction. In addition, the 'G-C-C-C' and 'G-C-G-C' haplotypes of EGR3 were overrepresented in the patients with BPD (p = 0.0055, < 0.0001, respectively) and the 'G-T-G-C' haplotype of EGR3 was underrepresented in patients with BPD (p = 0.0040). Conclusions In summary, our study supports the association of EGR3 with BPD in Korean population sample, and EGR3 could be suggested as a compelling susceptibility gene in BPD.

• 문화기술의 융합
• /
• 제9권1호
• /
• pp.571-580
• /
• 2023

본 연구는 Cakewalk By Bandlab(이하 Cakewalk)과 TTS-1의 접근성 평가의 후속연구로, Cakewalk가 기본으로 제공하는 VSTi 형식의 SI-Drum의 기본 접근성을 평가하고 접근방법을 연구하기 위한 목적이 있다. 본 연구에서는 스크린리더 사용자가 SI-Drum을 사용하여 국가직무능력표준(NCS) [가상악기 에디팅하기] 작업을 수행할 수 있는 지에 대한 기본 접근성이 소프트웨어 개발자 관점에서 평가되었다. 또한, Cakewalk 단축키 등록 기능, MIDI 기능, 센스리더 기능을 활용한 확장된 접근성이 평가되고 접근방법이 모색되었다. 본 연구 결과, SI-Drum의 기본 접근성은 없는 것으로 나타났으나, 센스리더의 마우스 기능과 위치기억 기능을 중심으로 사용하는 확장된 접근 기능을 활용하면 [가상악기 에디팅하기] 작업을 수행할 수 있는 것으로 나타났다. 하지만, 이 작업도 각 컨트롤에 접근할 수 있는 위치기억 목록을 만들고 저장하기 위한 정안인의 도움이 필요한 것으로 나타났다. 스크린리더 사용자가 더 효율적으로 작업을 수행하기 위해서는 기본 접근성 확보가 우선 필요하고, 이를 위한 Cakewalk와 센스리더의 개선사항이 제안되었다.

• Structural Engineering and Mechanics
• /
• 제85권4호
• /
• pp.469-484
• /
• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• International Journal of Computer Science & Network Security
• /
• 제24권5호
• /
• pp.53-63
• /
• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• 대한토목학회논문집
• /
• 제40권3호
• /
• pp.273-283
• /
• 2020

기후변화의 영향으로 국지성 및 집중호우에 대한 발생 가능성이 높아지는 시점에서 과거에 침수피해를 입은 도시 유역에 대하여 실제 호우에 대한 침수 양상을 예측하는 것은 중요하다. 이에 수치해석 기반 프로그램과 함께 기계학습을 이용한 홍수 분석에 대한 연구가 증가하고 있다. 본 연구에서 적용한 LSTM 신경망은 일련의 자료를 분석하는데 유용하지만, 딥 러닝을 수행하기 위하여 충분한 양의 자료를 필요로 한다. 그러나 단일 도시유역에 홍수를 일으킬 강우가 매년 일어나지 않기에 많은 홍수 자료를 수집하기에는 어려움이 있다. 이에 본 연구에서는 대상 유역에서 관측되는 강우 외에 전국 단위의 실제 호우를 예측 모형에 반영하였다. LSTM (Long Short-Term Memory) 신경망은 강우에 대한 총 월류량을 예측하기 위하여 사용되었으며, 목표값으로 SWMM (Storm Water Management Model)의 유출 모의 결과를 사용하였다. 침수 범위 예측을 위해서는 로지스틱 회귀를 사용하였으며, 로지스틱 회귀 모형의 독립 변수는 총 월류량이며 종속 변수는 격자 별 침수 발생 유무이다. 침수 범위 자료는 SWMM의 유출 결과를 바탕으로 수행된 2차원 침수해석 모의 결과를 통해 수집하였다. LSTM의 매개변수 조건에 따라 총 월류량 예측 결과를 비교하였다. 매개변수 설정에 따른 4가지의 LSTM 모형을 사용하였는데, 검증과 테스트 단계에 대한 평균 RMSE (Root Mean Square Error)는 1.4279 ㎥/s, 1.0079 ㎥/s으로 산정되었다. 최소 RMSE는 검증과 테스트에 대하여 각각 1.1656 ㎥/s, 0.8797㎥/s 으로 산정되었으며, SWMM모의 결과를 적절히 재현할 수 있음을 확인하였다. LSTM 신경망의 결과와 로지스틱 회귀를 연계하여 침수 범위 예측을 수행하였으며, 침수심 0.5m 이상을 고려하였을 때에 최대 침수면적 적합도가 97.33 %으로 나타났다. 본 연구에서 제시된 방법론은 딥 러닝에 기반하여 도시 홍수 대응능력을 향상 시키는데 도움이 될 것으로 판단된다.

• 동의신경정신과학회지
• /
• 제21권2호
• /
• pp.125-140
• /
• 2010

Objectives : The purpose of this study is to examine from various angles the protective effect of Gastrodia elata Blume (GEB) against nerve cell death induced by $\beta$-amyloid by using the cell line SH-SY5Y, which is commonly utilized for toxicity testing in nerve cells, and to find out its mechanism of action. Methods : To begin with, as a result of assessing the rate of cell survival by employing MTT reduction assay, the treatment with $\beta$-amyloid at different concentrations caused cytotoxicity, which was inhibited by preprocessing GEB extract. In addition, after $\beta$-amyloid was processed with the cell SH-SY5Y, apoptosis progressed, which was reduced effectively by processing GEB extract. Results : When cytotoxicity was caused by using hydrogen peroxide, a representative ROS, in order to examine the antioxidant effect of GEB, its protective effect was also observed. Apart from ROS, reactive nitrogen species (RNS) are also known to play a crucial role in nerve cell death. The treatment with the NO donor SNAP increased the production of nitric oxide and the expression of iNOS, which was also inhibited by GEB extract. Meanwhile, as an attempt to find out the mechanism of action explaining the antioxidant effect, the intracellular antioxidant enzyme expressions were measured by RT-PCR, which showed that GEB extract increased the expressions of heme oxygenase-1, GAPDH and $\gamma$-glutamate cysteine ligase. Lastly, GEB extract had a protective effect against impaired memory induced by scopolamine in animal models (in vivo). Conclusions : These findings indicate that GEB has a protective effect against the death of cranial nerve cells, suggesting possibilities for the prevention and treatment of AD.